Distance-Based Supervised Metric Learning for School Dropout Risk Identification in High School Students

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:49Z
Fecha de publicación2026-01-01
ResumenEducation is very important for a society’s economic, social, and cultural growth. However, educational systems still have structural problems that make it hard for students to stay in school and achieve academic success. One of the most significant problems in education is student dropout. This situation is especially impactful in high school, where its effects extend beyond the school and into long-term social and economic outcomes. This paper proposes an approach to identify data-driven indicators of dropout risk by using supervised learning and optimization methods. Our proposal consists of a supervised feature-weighted metric learning strategy that improves class separability in distance-based classifiers by reweighting features based on label information. To achieve the best possible k-nearest neighbors classification accuracy, we formulate metric learning as an optimization problem. Moreover, to optimize our proposal, we considered population-based, gradient-free metaheuristics. Furthermore, our proposed method preserves the original feature space to improve neighborhood relationships in contrast to traditional preprocessing or dimensionality reduction methods, which are important for educational outcomes. Actual school records from a high school in Ciudad Madero, Tamaulipas, Mexico, were used to conduct the experimentation to assess our proposal. Based on the experimental results, we observe an improvement in classification performance, with accuracy increasing from about 0.87 to 0.98. For statistical support, we applied a nonparametric Friedman test, which showed that these improvements are statistically significant. Hence, our proposal could be a useful and scalable method for educational data and support strategies for early identification of students at risk of dropout.es_ES
Doihttps://doi.org/10.3390/educsci16050783es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1769
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónEducation Scienceses_ES
URL relacionadohttps://doi.org/10.3390/educsci16050783es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteEducation Sciences
TítuloDistance-Based Supervised Metric Learning for School Dropout Risk Identification in High School Studentses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorHurtado-Mora, Hyasseliny A.
AutorPichardo-Ramírez, Roberto
AutorGarcía-Ruiz, Alejandro H.
AutorOrtega-Guzmán, Andrea
AutorHerrera-Barajas, Luis A.
AutorGonzález-del-Ángel, Luis J.
AutorHurtado-Mora, Hyasseliny A.es_ES
AutorPichardo-Ramírez, Robertoes_ES
AutorGarcía-Ruiz, Alejandro H.es_ES
AutorOrtega-Guzmán, Andreaes_ES
AutorHerrera-Barajas, Luis A.es_ES
AutorGonzález-del-Ángel, Luis J.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número5es_ES
Rango de páginas783es_ES
URL relacionadahttps://doi.org/10.3390/educsci16050783
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen16es_ES

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